Subjective Well-Being and Its Relation to Academic Performance among Students in Medicine, Dentistry, and Other Health Professions
Bibliographic record
Abstract
Subjective well-being is defined as a person’s cognitive and affective evaluations of his or her life. This study aims to investigate the differences in the domains of subjective well-being based on gender, type of school, and academic performance. Additionally, the study aimed to determine the factors (socio-demographic variables, including the academic performance of the students) that are predictive of subjective well-being. Subjective well-being was assessed using a questionnaire which included the Satisfaction with Life Scale (SWLS), which measured the respondent’s life satisfaction, the Scale of Positive and Negative Experience (SPANE), which consisted of six positive and negative emotions, and, lastly, the Flourishing Scale (FS), which measured the respondents’ self-perceived success. Data were collected, transformed into a linear scale, and exported into SPSS version 24, where t-tests, one-way analysis of variance, Pearson correlation, and stepwise regression were performed. Of the total of 535 participants, the majority were females (383 = 71.6%) and studying in a school of medicine (31.8%). With respect to the SWLS and FS, a significant difference was reported among students based on the type of school and their academic performance (p < 0.05). While comparing the differences in the SPANE, a significant difference was recorded based on academic performance. Among the domains of subjective well-being, only the SPANE showed a significant association with academic performance. Greater subjective well-being correlates with higher academic performance, indicating that subjective well-being is an important aspect of a student’s academic life; provisions can be made by paying more attention to those who showed poor academic performance during and at the end of each semester.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".